摘要
2020年初新冠肺炎疫情爆发以后,为了有效防止疫情扩散和蔓延,全国主要城市都相继停工停产,人类活动和污染排放强度大幅降低.本文针对新冠肺炎爆发前后南京地区污染排放变化,利用南京及周边地区的逐时污染观测资料,基于在线区域化学传输模式WRF-Chem和集合平方根滤波同化系统,在同时同化污染物浓度初始场和污染源排放的框架下,在1 km的高分辨率条件下进行了逐时同化实验模拟.结果表明,由于疫情管控,2月南京市区的PM2.5气溶胶和NO源排放分别较1月减少4.4%和30%,减少的区域主要位于工业区和南京城区.该结果显示同化系统在反演高时空分辨率的污染源方面具有很好的潜力.
The COVID-19 led to a stringent lockdown in most cities in China to effectively prevent the spread of the epidemic, resulting in reductions in air pollutant emissions. A high-resolution data assimilation experiment was carried out to invetigate the pollution emissions changes before and during the COVID-19 lockdown by adjusting the chemical initial conditions and the pollutant emissions simultaneously through the use of the online regional chemical transport model, WRF-Chem and an ensemble square-root system. Numerical results showed that PM2.5 emissions and NO emissions in February fell by about 4.4% and 30% respectively, compared to the emissions before the COVID-19 lockdown in Jan 2020. This work show that data assimilation has the potential to inverse emission with high-resolution.
作者
蔡菊珍
彭珍
孙鉴泞
谢郁宁
刘强
CAI Juzhen;PENG Zhen;SUN Jianning;XIE Yuning;LIU Qiang(Zhejiang Climate Center,Hangzhou 310017;School of Atmospheric Sciences,Nanjing University,Nanjing 210023;Nanjing Pulan Atmospheric Environment Research Institute Inc.,Nangjing 211800;Nanjing CLIMBLUE Technology Co.LTD.,Nanjing 211135)
出处
《环境科学学报》
CAS
CSCD
北大核心
2021年第8期2959-2965,共7页
Acta Scientiae Circumstantiae
基金
国家自然科学基金(No.41875014,41975004)。
